





'''simple docstring'''






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCAmelCase									=   """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
lowerCAmelCase									=   [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
lowerCAmelCase									=   dict(zip(vocab, range(len(vocab))))
lowerCAmelCase									=   ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCAmelCase									=   Path(tmpdirname)
					lowerCAmelCase									=   build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
					lowerCAmelCase									=   build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
					lowerCAmelCase									=   build_dir / VOCAB_FILES_NAMES["""merges_file"""]
					with open(src_vocab_file, """w""") as fp:
										fp.write(json.dumps(vocab_tokens))
					with open(tgt_vocab_file, """w""") as fp:
										fp.write(json.dumps(vocab_tokens))
					with open(merges_file, """w""") as fp:
										fp.write("""\n""".join(merges))

					lowerCAmelCase									=   FSMTTokenizer(
					    langs=["""en""", """ru"""],
					    src_vocab_size=len(vocab),
					    tgt_vocab_size=len(vocab),
					    src_vocab_file=src_vocab_file,
					    tgt_vocab_file=tgt_vocab_file,
					    merges_file=merges_file,
					)

lowerCAmelCase									=   FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCAmelCase									=   tokenizer(["""Making tiny model"""], return_tensors="""pt""")
lowerCAmelCase									=   tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru










'''simple docstring'''






lowerCAmelCase									=   [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Optional[int]       						= 0
		while number:
				# Increased Speed Slightly by checking every 5 digits together.
				sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
				number //= 1_0_0_0_0_0

		return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowerCAmelCase									=   [None] * 10_00_00_00
lowerCAmelCase									=   True
lowerCAmelCase									=   False






def   __A    (				a_    :	int						):

		if CHAINS[number - 1] is not None:
				return CHAINS[number - 1]  # type: ignore

		lowerCAmelCase  :     Dict       						= chain(next_number(a_						)						)
		lowerCAmelCase  :     Union[str, Any]       						= number_chain

		while number < 1_0_0_0_0_0_0_0:
				lowerCAmelCase  :     Any       						= number_chain
				number *= 1_0

		return number_chain






def   __A    (				a_    :	int = 1_0_0_0_0_0_0_0						):
		for i in range(1   ,a_						):
				if CHAINS[i] is None:
						chain(i + 1						)

		return CHAINS[:number].count(a_						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F'''{solution() = }''')



